Bayesian Network-Based Integrative Genomics Methods for Precision Medicine

基于贝叶斯网络的精准医学综合基因组学方法

基本信息

批准号：
10577871
负责人：
Veerabhadran Baladandayuthapani
金额：
$ 43.36万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-02-01 至 2025-01-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10577871
关键词：
Acceleration Area Bayesian Network Biological Biological Factors Biological Process Cancer Patient Cancer cell line Cell Line Characteristics Clinical Clinical Research Clustered Regularly Interspaced Short Palindromic Repeats Code Colorectal Cancer Communities Complex Computer software Consensus DNA Sequence Alteration Data Data Set Disease Drug Targeting Encyclopedias Epigenetic Process Follow-Up Studies Formulation Free Will Genes Genetic Genomics Graph Heterogeneity Immune Individual Knowledge Letters Link Literature Malignant Neoplasms Mediation Mediator Messenger RNA Methods Modeling Modernization Molecular Mutation Network-based Oncogenic Pathway interactions Patient Selection Patients Pharmaceutical Preparations Phenotype Precision therapeutics Prediction of Response to Therapy Process Research Research Personnel Resource Sharing Running Statistical Methods Structure Techniques Testing The Cancer Genome Atlas Therapeutic Training University of Texas M D Anderson Cancer Center Validation Work active method analytical method arm cancer subtypes clinical translation cohort colon cancer patients data sharing genomic data genomic platform improved outcome individual patient large datasets molecular subtypes network models new therapeutic target novel personalized medicine precision medicine preclinical study rare cancer response statistical learning success targeted treatment therapeutic target tool tumor usability web portal

项目摘要

Project Summary/Abstract Modern multi-platform genomic data sets contain substantial molecular information potentially useful for discovering new precision therapeutic strategies. Integration across multi-platform data and across genes using network-based models is a key to extracting mechanistic molecular information embedded in these data. In this proposal, we develop integrative network-based methods that ll gaps in existing literature. They will be used to identify key pathways for a given disease and its subtypes, nd key upstream regulators of these pathways and determine which appear to be causal, construct pathway signatures potentially usable for patient selection, and identify factors modulating pathway associations. While our methods will be applicable to any disease setting, our initial focus will be to use multi-platform genomic data sets to provide a deep molecular characterization of four recently discovered consensus molecular subtypes (CMS) of colorectal cancer (CRC) to arm our biomedical and clinical collaborators with knowledge to devise and test new precision therapeutic strategies targeting these subtypes. For these purposes, we propose the following aims: Speci c Aim 1: We will devise a novel model formulation regressing pathway scores on upstream genetic and epigenetic factors to identify a sparse set of potential pathway drivers. We will identify characteristic pathways for each CMS and for each pathway identify potential drivers that our biomedical collaborators will functionally validate via CRISPR and identify potential matching drug targets. We will also develop novel Bayesian hierarchically linked regression models (BLINK) that will determine which cancers share common pathway drivers and thus are candidates for sharing a common targeted therapy, while increasing power for discovery of pathway drivers for rare cancers. Speci c Aim 2: We will develop network mediation analysis approaches to discover putative causal network edges in multi-layered graphs of multi-platform genomic data. We will use these methods to more deeply characterize the networks underlying key CMS-characteristic pathways and determine which potential pathway drivers appear to be causal, and which mediators are predictive of response to therapy. From these networks, we will devise methods to construct pathway signatures integrating multi-platform molecular information to provide a single-number, patient- speci c summary of pathway activity potentially useful for patient selection for precision therapeutics. Speci c Aim 3: We will develop novel Bayesian network regression methods for undirected and multi-layer networks that identify heterogeneous network structure varying linearly or nonlinearly across patient-speci c covariates. We will apply these methods to key networks identi ed for CRC data to discover how these networks vary across various covariates, including subtypes (CMS), biological factors (immune in ltration), and clinical response. Successful completion of this work will produce a broad set of rigorous tools for integrative and network modeling of multi-platform genomic data, and will provide our CRC collaborators with a short list of key CMS-speci c pathways and drivers for functional validation and clinical translation via CMS-based precision therapeutics. Our dissemination eﬀorts will include software for our methods and Shiny apps for exploring biological underpinnings of CRC.

项目摘要/摘要现代的多平台基因组数据集包含可用于发现的大量分子信息新的精确理论策略。使用基于网络的多平台数据和基因跨基因集成模型是提取这些数据中嵌入的机械分子信息的关键。在此提案中，我们发展基于网络的基于网络的方法，在现有文献中差异。它们将用于识别关键途径给定疾病及其亚型，这些途径的关键上游调节剂，并确定哪个似乎是因果，构造途径签名可能可用于患者选择，并确定调节途径的因素协会。虽然我们的方法适用于任何疾病，但我们最初的重点是使用多平台基因组数据集可提供四个最近发现的共有分子亚型的深层分子表征（CMS）结直肠癌（CRC），武装我们的生物医学和临床合作者，以设计和测试针对这些亚型的新的精确理论策略。为了这些目的，我们提出以下目的：特定C AIM 1：我们将设计一个新型模型，该模型在上游遗传和表观遗传学上得分进行回归途径确定一组稀疏的潜在途径驱动因素的因素。我们将确定每个CM和对于每种途径，确定我们的生物医学合作者将通过CRISPR和确定潜在的匹配药物靶标。我们还将开发新颖的贝叶斯分层链接回归模型（眨眼）将确定哪些癌症共享通用途径驱动因素，因此是共享共同的候选人有针对性的疗法，同时增加了为罕见癌症发现途径驱动因素的功率。特定C AIM 2：我们将开发网络中介分析方法以发现推定的因果网络边缘在多平台基因组数据的多层图中。我们将使用这些方法更深入地描述钥匙CMS特征途径的基础网络，并确定哪些潜在途径驱动程序似乎是因果关系，哪些介体可以预测对治疗的反应。从这些网络中，我们将设计方法构建途径签名，集成多平台分子信息，以提供单数，患者 - 途径c活动的特定摘要可能对患者选择精确治疗可能有用。 Speci C AIM 3：我们将开发新颖的贝叶斯网络回归方法，用于无向和多层网络在患者特异性协变量中，识别异质网络结构线性或非线性变化。我们将这些方法应用于为CRC数据标识的关键网络，以发现这些网络在各种方面如何变化协变量，包括亚型（CMS），生物学因素（LTRATION中的免疫）和临床反应。这项工作的成功完成将产生一系列严格的工具，用于集成和网络建模多平台基因组数据，将为我们的CRC合作者提供关键CMS规格途径的简短列表和通过基于CMS的精确疗法进行功能验证和临床翻译的驱动因素。我们的传播效果将包括用于我们方法的软件和用于探索CRC生物基础的闪亮应用程序。